In deterministic partially observable worlds, perfect prediction requires either identifying the relevant hidden quotient or achieving overwrite control, while high empowerment alone is insufficient.
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IBM Journal of Research and Development5(3), 183–191 (Jul 1961)
6 Pith papers cite this work, alongside 4,171 external citations. Polarity classification is still indexing.
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Statistical analysis of energy data falsifies the 1% exponential growth in the Kardashev model, shows linear extrapolation yields a 1.6E15-year Type II timescale, and introduces the KSN renormalization B(t) = P(t)/H(t) spanning 14 orders of magnitude.
A crumbling abstract machine yields a reversible Landauer's embedding for call-by-value lambda calculus with constant space overhead per step.
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
A thermodynamic-inspired information-geometric framework defines a composite LLM stability score that outperforms a utility-entropy baseline by 0.0299 on average across 80 observations, with gains increasing at higher entropy.
The Kerimov-Alekberli model uses KL divergence on a Riemannian manifold with a Fisher-derived threshold and the Landauer principle to treat adversarial perturbations as measurable physical work for real-time AI system stability.
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Prediction and Empowerment: A Theory of Agency through Bridge Interfaces
In deterministic partially observable worlds, perfect prediction requires either identifying the relevant hidden quotient or achieving overwrite control, while high empowerment alone is insufficient.
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Kardashev's Conundrum: Statistical Falsification of the Standard Kardashev Model and the Kardashev--Sagan--Nakamoto Resolution
Statistical analysis of energy data falsifies the 1% exponential growth in the Kardashev model, shows linear extrapolation yields a 1.6E15-year Type II timescale, and introduces the KSN renormalization B(t) = P(t)/H(t) spanning 14 orders of magnitude.
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A Reversible Crumbling Abstract Machine for Plotkin's Call-by-Value
A crumbling abstract machine yields a reversible Landauer's embedding for call-by-value lambda calculus with constant space overhead per step.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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An Information-Geometric Framework for Stability Analysis of Large Language Models under Entropic Stress
A thermodynamic-inspired information-geometric framework defines a composite LLM stability score that outperforms a utility-entropy baseline by 0.0299 on average across 80 observations, with gains increasing at higher entropy.
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The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability
The Kerimov-Alekberli model uses KL divergence on a Riemannian manifold with a Fisher-derived threshold and the Landauer principle to treat adversarial perturbations as measurable physical work for real-time AI system stability.